M.Sc. Computer Science

Machine Learning covers the core supervised and unsupervised learning methods, how to evaluate them honestly, and where they tend to fail. Every technique is paired with a discussion of its assumptions, so students learn to ask what a model actually knows, not just what it outputs.

Artificial Intelligence Foundations introduces the core problem-solving paradigms of AI: search, knowledge representation, and an introduction to learning-based approaches, always with attention to what a technique can and cannot reasonably claim to do.

Cloud Computing & Systems at Scale examines how modern applications are deployed, scaled, and kept resilient across managed infrastructure. Students design and deploy a small application under simulated load, then diagnose and fix the bottlenecks that appear.

Deep Learning & Neural Networks goes past the introductory treatment in CSC304 into architectures for images, sequences, and beyond, along with the practical craft of actually training these models: initialization, regularization, and diagnosing why a network refuses to learn.